Modern Deep Learning (DL) models continue to drive exciting advances across several medical imaging tasks, from image reconstruction and enhancement [1]}, [2]}, [3]}, [4]}, [5]}, to automatic lesion detection and segmentation [6]}, [7]}, [8]}. DL models for classification and detection are especially desirable for Computer-Aided Diagnosis (CAD) systems in radiology, potentially supporting clinicians in their decision-making by providing a second opinion on subtle cases, or prioritizing the most severe ones [9]}, [10]}, [11]}. Indeed, recent results indicate that the performance of these machine learning models can be comparable to that of expert physicians in many scenarios [12]}, [13]}, and they hold significant promise for the automation of diagnosis, especially in underserved areas where access to radiology expertise might be limited [14]}, [15]}, [16]}, [17]}, [18]}, [19]}.
